Hybrid Algorithm for Clustering of Microarray Data

Clustering is a crucial step in the analysis of gene expression data. Its goal is to identify the natural clusters and provide a reliable estimate of the number of distinct clusters in a given data set. In this paper we propose new hybrid algorithm for clustering of microarray data based on spectral clustering and k-means. Our algorithm consist of four steps, including preprocessing or filtering step, and finding optimal number of clusters by using two different clustering methods based on hierarchical and partition-based approaches. Then, we cluster data based on similarity/dissimilarity metrics with spectral clustering. In the final step, we select centroid genes based on kmeans results. The proposed method was tested on six data sets from GEMS microarray database. When compared with existing single or combination of clustering methods, our results indicate about 10% improvement in selection of representative genes.

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